Merge branch 'master' into fix/windows-quoting

.github/ISSUE_TEMPLATE/01_bugs.md

@@ -10,7 +10,7 @@ about: Use this template if you came across a bug or unexpected behaviour differ
 <!-- Include a code example or the steps that led to the problem. Please try to be as specific as possible. -->
 
 ## Your Environment
-<!-- Include details of your environment. If you're using spaCy 1.7+, you can also type `python -m spacy info --markdown` and copy-paste the result here.-->
+<!-- Include details of your environment. You can also type `python -m spacy info --markdown` and copy-paste the result here.-->
 * Operating System:
 * Python Version Used:
 * spaCy Version Used:

.github/azure-steps.yml

@@ -27,7 +27,7 @@ steps:
 
   - script: python -m mypy spacy
     displayName: 'Run mypy'
-    condition: ne(variables['python_version'], '3.10')
+    condition: ne(variables['python_version'], '3.6')
 
   - task: DeleteFiles@1
     inputs:

.pre-commit-config.yaml

@@ -6,7 +6,7 @@ repos:
       language_version: python3.7
       additional_dependencies: ['click==8.0.4']
 -   repo: https://gitlab.com/pycqa/flake8
-    rev: 3.9.2
+    rev: 5.0.4
     hooks:
     - id: flake8
       args:

azure-pipelines.yml

@@ -31,7 +31,7 @@ jobs:
         inputs:
           versionSpec: "3.7"
       - script: |
-          pip install flake8==3.9.2
+          pip install flake8==5.0.4
           python -m flake8 spacy --count --select=E901,E999,F821,F822,F823,W605 --show-source --statistics
         displayName: "flake8"
 

requirements.txt

@@ -15,7 +15,7 @@ pathy>=0.3.5
 numpy>=1.15.0
 requests>=2.13.0,<3.0.0
 tqdm>=4.38.0,<5.0.0
-pydantic>=1.7.4,!=1.8,!=1.8.1,<1.10.0
+pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0
 jinja2
 langcodes>=3.2.0,<4.0.0
 # Official Python utilities
@@ -28,9 +28,9 @@ cython>=0.25,<3.0
 pytest>=5.2.0,!=7.1.0
 pytest-timeout>=1.3.0,<2.0.0
 mock>=2.0.0,<3.0.0
-flake8>=3.8.0,<3.10.0
+flake8>=3.8.0,<6.0.0
 hypothesis>=3.27.0,<7.0.0
-mypy>=0.910,<0.970; platform_machine!='aarch64'
+mypy>=0.980,<0.990; platform_machine != "aarch64" and python_version >= "3.7"
 types-dataclasses>=0.1.3; python_version < "3.7"
 types-mock>=0.1.1
 types-setuptools>=57.0.0

setup.cfg

@@ -56,7 +56,7 @@ install_requires =
     tqdm>=4.38.0,<5.0.0
     numpy>=1.15.0
     requests>=2.13.0,<3.0.0
-    pydantic>=1.7.4,!=1.8,!=1.8.1,<1.10.0
+    pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0
     jinja2
     # Official Python utilities
     setuptools

spacy/cli/_util.py

@@ -573,3 +573,12 @@ def setup_gpu(use_gpu: int, silent=None) -> None:
         local_msg.info("Using CPU")
         if gpu_is_available():
             local_msg.info("To switch to GPU 0, use the option: --gpu-id 0")
+
+
+def _format_number(number: Union[int, float], ndigits: int = 2) -> str:
+    """Formats a number (float or int) rounding to `ndigits`, without truncating trailing 0s,
+    as happens with `round(number, ndigits)`"""
+    if isinstance(number, float):
+        return f"{number:.{ndigits}f}"
+    else:
+        return str(number)

spacy/cli/debug_data.py

@@ -9,7 +9,7 @@ import typer
 import math
 
 from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
-from ._util import import_code, debug_cli
+from ._util import import_code, debug_cli, _format_number
 from ..training import Example, remove_bilu_prefix
 from ..training.initialize import get_sourced_components
 from ..schemas import ConfigSchemaTraining
@@ -989,7 +989,8 @@ def _get_kl_divergence(p: Counter, q: Counter) -> float:
 def _format_span_row(span_data: List[Dict], labels: List[str]) -> List[Any]:
     """Compile into one list for easier reporting"""
     d = {
-        label: [label] + list(round(d[label], 2) for d in span_data) for label in labels
+        label: [label] + list(_format_number(d[label]) for d in span_data)
+        for label in labels
     }
     return list(d.values())
 
@@ -1004,6 +1005,10 @@ def _get_span_characteristics(
         label: _gmean(l)
         for label, l in compiled_gold["spans_length"][spans_key].items()
     }
+    spans_per_type = {
+        label: len(spans)
+        for label, spans in compiled_gold["spans_per_type"][spans_key].items()
+    }
     min_lengths = [min(l) for l in compiled_gold["spans_length"][spans_key].values()]
     max_lengths = [max(l) for l in compiled_gold["spans_length"][spans_key].values()]
 
@@ -1031,6 +1036,7 @@ def _get_span_characteristics(
     return {
         "sd": span_distinctiveness,
         "bd": sb_distinctiveness,
+        "spans_per_type": spans_per_type,
         "lengths": span_length,
         "min_length": min(min_lengths),
         "max_length": max(max_lengths),
@@ -1045,12 +1051,15 @@ def _get_span_characteristics(
 
 def _print_span_characteristics(span_characteristics: Dict[str, Any]):
     """Print all span characteristics into a table"""
-    headers = ("Span Type", "Length", "SD", "BD")
+    headers = ("Span Type", "Length", "SD", "BD", "N")
+    # Wasabi has this at 30 by default, but we might have some long labels
+    max_col = max(30, max(len(label) for label in span_characteristics["labels"]))
     # Prepare table data with all span characteristics
     table_data = [
         span_characteristics["lengths"],
         span_characteristics["sd"],
         span_characteristics["bd"],
+        span_characteristics["spans_per_type"],
     ]
     table = _format_span_row(
         span_data=table_data, labels=span_characteristics["labels"]
@@ -1061,8 +1070,18 @@ def _print_span_characteristics(span_characteristics: Dict[str, Any]):
         span_characteristics["avg_sd"],
         span_characteristics["avg_bd"],
     ]
-    footer = ["Wgt. Average"] + [str(round(f, 2)) for f in footer_data]
+
-    msg.table(table, footer=footer, header=headers, divider=True)
+    footer = (
+        ["Wgt. Average"] + ["{:.2f}".format(round(f, 2)) for f in footer_data] + ["-"]
+    )
+    msg.table(
+        table,
+        footer=footer,
+        header=headers,
+        divider=True,
+        aligns=["l"] + ["r"] * (len(footer_data) + 1),
+        max_col=max_col,
+    )
 
 
 def _get_spans_length_freq_dist(

spacy/cli/package.py

@@ -299,8 +299,8 @@ def get_meta(
     }
     nlp = util.load_model_from_path(Path(model_path))
     meta.update(nlp.meta)
-    meta.update(existing_meta)
     meta["spacy_version"] = util.get_minor_version_range(about.__version__)
+    meta.update(existing_meta)
     meta["vectors"] = {
         "width": nlp.vocab.vectors_length,
         "vectors": len(nlp.vocab.vectors),

spacy/cli/project/dvc.py

@@ -110,9 +110,6 @@ def update_dvc_config(
     config_commands = {cmd["name"]: cmd for cmd in config.get("commands", [])}
 
     # some flags that apply to every command
-    if verbose and quiet:
-        # don't allow contradictions
-        msg.fail("Can't set both --verbose and --quiet", exits=1)
     flags = []
     if verbose:
         flags.append("--verbose")

spacy/lang/ru/lemmatizer.py

@@ -23,7 +23,7 @@ class RussianLemmatizer(Lemmatizer):
         overwrite: bool = False,
         scorer: Optional[Callable] = lemmatizer_score,
     ) -> None:
-        if mode == "pymorphy2":
+        if mode in {"pymorphy2", "pymorphy2_lookup"}:
             try:
                 from pymorphy2 import MorphAnalyzer
             except ImportError:

spacy/lang/uk/lemmatizer.py

@@ -18,7 +18,7 @@ class UkrainianLemmatizer(RussianLemmatizer):
         overwrite: bool = False,
         scorer: Optional[Callable] = lemmatizer_score,
     ) -> None:
-        if mode == "pymorphy2":
+        if mode in {"pymorphy2", "pymorphy2_lookup"}:
             try:
                 from pymorphy2 import MorphAnalyzer
             except ImportError:

spacy/pipeline/edit_tree_lemmatizer.py

@@ -1,7 +1,6 @@
 from typing import cast, Any, Callable, Dict, Iterable, List, Optional
-from typing import Sequence, Tuple, Union
+from typing import Tuple
 from collections import Counter
-from copy import deepcopy
 from itertools import islice
 import numpy as np
 
@@ -149,9 +148,7 @@ class EditTreeLemmatizer(TrainablePipe):
         if not any(len(doc) for doc in docs):
             # Handle cases where there are no tokens in any docs.
             n_labels = len(self.cfg["labels"])
-            guesses: List[Ints2d] = [
+            guesses: List[Ints2d] = [self.model.ops.alloc2i(0, n_labels) for _ in docs]
-                self.model.ops.alloc((0, n_labels), dtype="i") for doc in docs
-            ]
             assert len(guesses) == n_docs
             return guesses
         scores = self.model.predict(docs)

spacy/pipeline/entityruler.py

@@ -1,6 +1,5 @@
-import warnings
 from typing import Optional, Union, List, Dict, Tuple, Iterable, Any, Callable, Sequence
-from typing import cast
+import warnings
 from collections import defaultdict
 from pathlib import Path
 import srsly
@@ -317,7 +316,7 @@ class EntityRuler(Pipe):
                     phrase_pattern["id"] = ent_id
                 phrase_patterns.append(phrase_pattern)
             for entry in token_patterns + phrase_patterns:  # type: ignore[operator]
-                label = entry["label"]
+                label = entry["label"]  # type: ignore
                 if "id" in entry:
                     ent_label = label
                     label = self._create_label(label, entry["id"])

spacy/pipeline/spancat.py

@@ -133,6 +133,9 @@ def make_spancat(
     spans_key (str): Key of the doc.spans dict to save the spans under. During
         initialization and training, the component will look for spans on the
         reference document under the same key.
+    scorer (Optional[Callable]): The scoring method. Defaults to
+        Scorer.score_spans for the Doc.spans[spans_key] with overlapping
+        spans allowed.
     threshold (float): Minimum probability to consider a prediction positive.
         Spans with a positive prediction will be saved on the Doc. Defaults to
         0.5.

spacy/pipeline/textcat_multilabel.py

@@ -96,8 +96,8 @@ def make_multilabel_textcat(
     model: Model[List[Doc], List[Floats2d]],
     threshold: float,
     scorer: Optional[Callable],
-) -> "TextCategorizer":
+) -> "MultiLabel_TextCategorizer":
-    """Create a TextCategorizer component. The text categorizer predicts categories
+    """Create a MultiLabel_TextCategorizer component. The text categorizer predicts categories
     over a whole document. It can learn one or more labels, and the labels are considered
     to be non-mutually exclusive, which means that there can be zero or more labels
     per doc).
@@ -105,6 +105,7 @@ def make_multilabel_textcat(
     model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
         scores for each category.
     threshold (float): Cutoff to consider a prediction "positive".
+    scorer (Optional[Callable]): The scoring method.
     """
     return MultiLabel_TextCategorizer(
         nlp.vocab, model, name, threshold=threshold, scorer=scorer
@@ -147,6 +148,7 @@ class MultiLabel_TextCategorizer(TextCategorizer):
         name (str): The component instance name, used to add entries to the
             losses during training.
         threshold (float): Cutoff to consider a prediction "positive".
+        scorer (Optional[Callable]): The scoring method.
 
         DOCS: https://spacy.io/api/textcategorizer#init
         """

spacy/schemas.py

@@ -181,12 +181,12 @@ class TokenPatternNumber(BaseModel):
     IS_SUBSET: Optional[List[StrictInt]] = Field(None, alias="is_subset")
     IS_SUPERSET: Optional[List[StrictInt]] = Field(None, alias="is_superset")
     INTERSECTS: Optional[List[StrictInt]] = Field(None, alias="intersects")
-    EQ: Union[StrictInt, StrictFloat] = Field(None, alias="==")
+    EQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="==")
-    NEQ: Union[StrictInt, StrictFloat] = Field(None, alias="!=")
+    NEQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="!=")
-    GEQ: Union[StrictInt, StrictFloat] = Field(None, alias=">=")
+    GEQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias=">=")
-    LEQ: Union[StrictInt, StrictFloat] = Field(None, alias="<=")
+    LEQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="<=")
-    GT: Union[StrictInt, StrictFloat] = Field(None, alias=">")
+    GT: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias=">")
-    LT: Union[StrictInt, StrictFloat] = Field(None, alias="<")
+    LT: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="<")
 
     class Config:
         extra = "forbid"
@@ -430,7 +430,7 @@ class ProjectConfigAssetURL(BaseModel):
     # fmt: off
     dest: StrictStr = Field(..., title="Destination of downloaded asset")
     url: Optional[StrictStr] = Field(None, title="URL of asset")
-    checksum: str = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
+    checksum: Optional[str] = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
     description: StrictStr = Field("", title="Description of asset")
     # fmt: on
 
@@ -438,7 +438,7 @@ class ProjectConfigAssetURL(BaseModel):
 class ProjectConfigAssetGit(BaseModel):
     # fmt: off
     git: ProjectConfigAssetGitItem = Field(..., title="Git repo information")
-    checksum: str = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
+    checksum: Optional[str] = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
     description: Optional[StrictStr] = Field(None, title="Description of asset")
     # fmt: on
 
@@ -508,9 +508,9 @@ class DocJSONSchema(BaseModel):
         None, title="Indices of sentences' start and end indices"
     )
     text: StrictStr = Field(..., title="Document text")
-    spans: Dict[StrictStr, List[Dict[StrictStr, Union[StrictStr, StrictInt]]]] = Field(
+    spans: Optional[
-        None, title="Span information - end/start indices, label, KB ID"
+        Dict[StrictStr, List[Dict[StrictStr, Union[StrictStr, StrictInt]]]]
-    )
+    ] = Field(None, title="Span information - end/start indices, label, KB ID")
     tokens: List[Dict[StrictStr, Union[StrictStr, StrictInt]]] = Field(
         ..., title="Token information - ID, start, annotations"
     )

spacy/tests/conftest.py

@@ -343,6 +343,14 @@ def ru_lemmatizer():
     return get_lang_class("ru")().add_pipe("lemmatizer")
 
 
+@pytest.fixture
+def ru_lookup_lemmatizer():
+    pytest.importorskip("pymorphy2")
+    return get_lang_class("ru")().add_pipe(
+        "lemmatizer", config={"mode": "pymorphy2_lookup"}
+    )
+
+
 @pytest.fixture(scope="session")
 def sa_tokenizer():
     return get_lang_class("sa")().tokenizer
@@ -422,6 +430,15 @@ def uk_lemmatizer():
     return get_lang_class("uk")().add_pipe("lemmatizer")
 
 
+@pytest.fixture
+def uk_lookup_lemmatizer():
+    pytest.importorskip("pymorphy2")
+    pytest.importorskip("pymorphy2_dicts_uk")
+    return get_lang_class("uk")().add_pipe(
+        "lemmatizer", config={"mode": "pymorphy2_lookup"}
+    )
+
+
 @pytest.fixture(scope="session")
 def ur_tokenizer():
     return get_lang_class("ur")().tokenizer

spacy/tests/lang/ru/test_lemmatizer.py

@@ -78,3 +78,17 @@ def test_ru_lemmatizer_punct(ru_lemmatizer):
     assert ru_lemmatizer.pymorphy2_lemmatize(doc[0]) == ['"']
     doc = Doc(ru_lemmatizer.vocab, words=["»"], pos=["PUNCT"])
     assert ru_lemmatizer.pymorphy2_lemmatize(doc[0]) == ['"']
+
+
+def test_ru_doc_lookup_lemmatization(ru_lookup_lemmatizer):
+    words = ["мама", "мыла", "раму"]
+    pos = ["NOUN", "VERB", "NOUN"]
+    morphs = [
+        "Animacy=Anim|Case=Nom|Gender=Fem|Number=Sing",
+        "Aspect=Imp|Gender=Fem|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act",
+        "Animacy=Anim|Case=Acc|Gender=Fem|Number=Sing",
+    ]
+    doc = Doc(ru_lookup_lemmatizer.vocab, words=words, pos=pos, morphs=morphs)
+    doc = ru_lookup_lemmatizer(doc)
+    lemmas = [token.lemma_ for token in doc]
+    assert lemmas == ["мама", "мыла", "раму"]

spacy/tests/lang/uk/test_lemmatizer.py

@@ -9,3 +9,11 @@ def test_uk_lemmatizer(uk_lemmatizer):
     """Check that the default uk lemmatizer runs."""
     doc = Doc(uk_lemmatizer.vocab, words=["a", "b", "c"])
     uk_lemmatizer(doc)
+    assert [token.lemma for token in doc]
+
+
+def test_uk_lookup_lemmatizer(uk_lookup_lemmatizer):
+    """Check that the lookup uk lemmatizer runs."""
+    doc = Doc(uk_lookup_lemmatizer.vocab, words=["a", "b", "c"])
+    uk_lookup_lemmatizer(doc)
+    assert [token.lemma for token in doc]

website/docs/api/language.md

@@ -164,6 +164,9 @@ examples, see the
 Apply the pipeline to some text. The text can span multiple sentences, and can
 contain arbitrary whitespace. Alignment into the original string is preserved.
 
+Instead of text, a `Doc` can be passed as input, in which case tokenization is
+skipped, but the rest of the pipeline is run.
+
 > #### Example
 >
 > ```python
@@ -173,7 +176,7 @@ contain arbitrary whitespace. Alignment into the original string is preserved.
 
 | Name            | Description                                                                                                                                    |
 | --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
-| `text`          | The text to be processed. ~~str~~                                                                                                              |
+| `text`          | The text to be processed, or a Doc. ~~Union[str, Doc]~~                                                                                        |
 | _keyword-only_  |                                                                                                                                                |
 | `disable`       | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). ~~List[str]~~                                                |
 | `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
@@ -184,6 +187,9 @@ contain arbitrary whitespace. Alignment into the original string is preserved.
 Process texts as a stream, and yield `Doc` objects in order. This is usually
 more efficient than processing texts one-by-one.
 
+Instead of text, a `Doc` object can be passed as input. In this case
+tokenization is skipped but the rest of the pipeline is run.
+
 > #### Example
 >
 > ```python
@@ -194,7 +200,7 @@ more efficient than processing texts one-by-one.
 
 | Name                                       | Description                                                                                                                                                         |
 | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `texts`                                    | A sequence of strings. ~~Iterable[str]~~                                                                                                                            |
+| `texts`                                    | A sequence of strings (or `Doc` objects). ~~Iterable[Union[str, Doc]]~~                                                                                             |
 | _keyword-only_                             |                                                                                                                                                                     |
 | `as_tuples`                                | If set to `True`, inputs should be a sequence of `(text, context)` tuples. Output will then be a sequence of `(doc, context)` tuples. Defaults to `False`. ~~bool~~ |
 | `batch_size`                               | The number of texts to buffer. ~~Optional[int]~~                                                                                                                    |

website/meta/universe.json

@@ -1,5 +1,62 @@
 {
     "resources": [
+        {
+            "id": "Zshot",
+            "title": "Zshot",
+            "slogan": "Zero and Few shot named entity & relationships recognition",
+            "github": "ibm/zshot",
+            "pip": "zshot",
+            "code_example": [
+                "import spacy",
+                "from zshot import PipelineConfig, displacy",
+                "from zshot.linker import LinkerRegen",
+                "from zshot.mentions_extractor import MentionsExtractorSpacy",
+                "from zshot.utils.data_models import Entity",
+                "",
+                "nlp = spacy.load('en_core_web_sm')",
+                "# zero shot definition of entities",
+                "nlp_config = PipelineConfig(",
+                "    mentions_extractor=MentionsExtractorSpacy(),",
+                "    linker=LinkerRegen(),",
+                "    entities=[",
+                "        Entity(name='Paris',",
+                "               description='Paris is located in northern central France, in a north-bending arc of the river Seine'),",
+                "        Entity(name='IBM',",
+                "               description='International Business Machines Corporation (IBM) is an American multinational technology corporation headquartered in Armonk, New York'),",
+                "        Entity(name='New York', description='New York is a city in U.S. state'),",
+                "        Entity(name='Florida', description='southeasternmost U.S. state'),",
+                "        Entity(name='American',",
+                "              description='American, something of, from, or related to the United States of America, commonly known as the United States or America'),",
+                "        Entity(name='Chemical formula',",
+                "               description='In chemistry, a chemical formula is a way of presenting information about the chemical proportions of atoms that constitute a particular chemical compound or molecul'),",
+                "        Entity(name='Acetamide',",
+                "               description='Acetamide (systematic name: ethanamide) is an organic compound with the formula CH3CONH2. It is the simplest amide derived from acetic acid. It finds some use as a plasticizer and as an industrial solvent.'),",
+                "        Entity(name='Armonk',",
+                "               description='Armonk is a hamlet and census-designated place (CDP) in the town of North Castle, located in Westchester County, New York, United States.'),",
+                "        Entity(name='Acetic Acid',",
+                "               description='Acetic acid, systematically named ethanoic acid, is an acidic, colourless liquid and organic compound with the chemical formula CH3COOH'),",
+                "        Entity(name='Industrial solvent',",
+                "               description='Acetamide (systematic name: ethanamide) is an organic compound with the formula CH3CONH2. It is the simplest amide derived from acetic acid. It finds some use as a plasticizer and as an industrial solvent.'),",
+                "    ]",
+                ")",
+                "nlp.add_pipe('zshot', config=nlp_config, last=True)",
+                "",
+                "text = 'International Business Machines Corporation (IBM) is an American multinational technology corporation' \\",
+                "        ' headquartered in Armonk, New York, with operations in over 171 countries.'",
+                "",
+                "doc = nlp(text)",
+                "displacy.serve(doc, style='ent')"
+            ],
+            "thumb": "https://ibm.github.io/zshot/img/graph.png",
+            "url": "https://ibm.github.io/zshot/",
+            "author": "IBM Research",
+            "author_links": {
+                "github": "ibm",
+                "twitter": "IBMResearch",
+                "website": "https://research.ibm.com/labs/ireland/"
+            },
+            "category": ["scientific", "models", "research"]
+        },
         {
             "id": "concepcy",
             "title": "concepCy",
@@ -2403,20 +2460,20 @@
                 "import spacy",
                 "from spacy_wordnet.wordnet_annotator import WordnetAnnotator ",
                 "",
-                "# Load an spacy model (supported models are \"es\" and \"en\") ",
+                "# Load a spaCy model (supported languages are \"es\" and \"en\") ",
-                "nlp = spacy.load('en')",
+                "nlp = spacy.load('en_core_web_sm')",
-                "# Spacy 3.x",
+                "# spaCy 3.x",
-                "nlp.add_pipe(\"spacy_wordnet\", after='tagger', config={'lang': nlp.lang})",
+                "nlp.add_pipe(\"spacy_wordnet\", after='tagger')",
-                "# Spacy 2.x",
+                "# spaCy 2.x",
                 "# nlp.add_pipe(WordnetAnnotator(nlp.lang), after='tagger')",
                 "token = nlp('prices')[0]",
                 "",
-                "# wordnet object link spacy token with nltk wordnet interface by giving acces to",
+                "# WordNet object links spaCy token with NLTK WordNet interface by giving access to",
                 "# synsets and lemmas ",
                 "token._.wordnet.synsets()",
                 "token._.wordnet.lemmas()",
                 "",
-                "# And automatically tags with wordnet domains",
+                "# And automatically add info about WordNet domains",
                 "token._.wordnet.wordnet_domains()"
             ],
             "author": "recognai",